Expressing probabilistic graphical models in RCC

Abstract

The purpose of this paper is to show the expressiveness of two different formalisms that combine logic and probabilistic reasoning: Stochastic Logic Programs (SLPs) and Probabilistic Concurrent Constraint Programs (PCCs). We analyse the relation between the two and we show that we are able to express, using PCC programs, some of the main probabilistic graphical models: Bayesian Networks, Markov random fields, Markov chains, Hidden Markov models, Stochastic Context Free Grammars and Markov Logic Networks. We express this last framework also in SLPs.